In this section, we conduct the experiments on two datasets, including the SNIPS dataset and our new Twitter dataset. The SNIPS dataset has a total of 13,784 training utterances and 700 test utterances. For the Twitter dataset, we split 70\(\%\)  of the collected data (620 tweets) into the training dataset and the remaining 30\(\%\)  (265 tweets) into the test dataset. The OOD slots in the SNIPS dataset are \(object\_name\)  and \(movie\_name\) , while the OOD slot in our Twitter dataset is \(Aux\) . As shown in Table REF , the OOV percentage of SNIPS is approximately 2.7\(\%\) , and that of the Twitter dataset is approximately 26.5\(\%\) . Similarly, 5.2\(\%\)  of the SNIPS dataset consists of OOD patterns, while more than 23.6\(\%\)  of the new Twitter dataset is composed of OOD patterns by simply considering the tokens with the tag “\(Aux\) ”. We can see that there is a large difference in the ratios of OOD patterns and OOV tokens between the two datasets and that it is definitely more difficult to obtain a decent F1 accuracy on the new Twitter dataset. We implement our new pipeline on several state-of-the-art SLU models and compare its performance with the performance without using our expansion of the training dataset and our multistep inference algorithm. The baseline models are the Attention BiRNN model [1]}, the Slot-Gated BiRNN model [2]} and the Bi-model BiLSTM model [3]}, all of which obtain a state-of-art performance on the ATIS and SNIPS SLU datasets.
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